// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_AUTODIFF_JACOBIAN_H
#define EIGEN_AUTODIFF_JACOBIAN_H

namespace Eigen {

template <typename Functor> class AutoDiffJacobian : public Functor
{
public:
    AutoDiffJacobian() : Functor() {}
    AutoDiffJacobian(const Functor& f) : Functor(f) {}

    // forward constructors
#if EIGEN_HAS_VARIADIC_TEMPLATES
    template <typename... T> AutoDiffJacobian(const T&... Values) : Functor(Values...) {}
#else
    template <typename T0> AutoDiffJacobian(const T0& a0) : Functor(a0) {}
    template <typename T0, typename T1> AutoDiffJacobian(const T0& a0, const T1& a1) : Functor(a0, a1) {}
    template <typename T0, typename T1, typename T2> AutoDiffJacobian(const T0& a0, const T1& a1, const T2& a2) : Functor(a0, a1, a2) {}
#endif

    typedef typename Functor::InputType InputType;
    typedef typename Functor::ValueType ValueType;
    typedef typename ValueType::Scalar Scalar;

    enum
    {
        InputsAtCompileTime = InputType::RowsAtCompileTime,
        ValuesAtCompileTime = ValueType::RowsAtCompileTime
    };

    typedef Matrix<Scalar, ValuesAtCompileTime, InputsAtCompileTime> JacobianType;
    typedef typename JacobianType::Index Index;

    typedef Matrix<Scalar, InputsAtCompileTime, 1> DerivativeType;
    typedef AutoDiffScalar<DerivativeType> ActiveScalar;

    typedef Matrix<ActiveScalar, InputsAtCompileTime, 1> ActiveInput;
    typedef Matrix<ActiveScalar, ValuesAtCompileTime, 1> ActiveValue;

#if EIGEN_HAS_VARIADIC_TEMPLATES
    // Some compilers don't accept variadic parameters after a default parameter,
    // i.e., we can't just write _jac=0 but we need to overload operator():
    EIGEN_STRONG_INLINE
    void operator()(const InputType& x, ValueType* v) const { this->operator()(x, v, 0); }
    template <typename... ParamsType> void operator()(const InputType& x, ValueType* v, JacobianType* _jac, const ParamsType&... Params) const
#else
    void operator()(const InputType& x, ValueType* v, JacobianType* _jac = 0) const
#endif
    {
        eigen_assert(v != 0);

        if (!_jac)
        {
#if EIGEN_HAS_VARIADIC_TEMPLATES
            Functor::operator()(x, v, Params...);
#else
            Functor::operator()(x, v);
#endif
            return;
        }

        JacobianType& jac = *_jac;

        ActiveInput ax = x.template cast<ActiveScalar>();
        ActiveValue av(jac.rows());

        if (InputsAtCompileTime == Dynamic)
            for (Index j = 0; j < jac.rows(); j++) av[j].derivatives().resize(x.rows());

        for (Index i = 0; i < jac.cols(); i++) ax[i].derivatives() = DerivativeType::Unit(x.rows(), i);

#if EIGEN_HAS_VARIADIC_TEMPLATES
        Functor::operator()(ax, &av, Params...);
#else
        Functor::operator()(ax, &av);
#endif

        for (Index i = 0; i < jac.rows(); i++)
        {
            (*v)[i] = av[i].value();
            jac.row(i) = av[i].derivatives();
        }
    }
};

}  // namespace Eigen

#endif  // EIGEN_AUTODIFF_JACOBIAN_H
